activity
20232025
most citedMaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)

10 citations · 12 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

FlexiReg: Flexible Urban Region Representation Learning

Fengze Sun, Yanchuan Chang, Egemen Tanin +2

The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks s…

cs.LG2025

TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

Yanchuan Chang, Dingyang Lyu, Xu Cai +2

Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity…

cs.LG2024★ 2 cited

DualCast: A Model to Disentangle Aperiodic Events from Traffic Series

Xinyu Su, Feng Liu, Yanchuan Chang +3

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the traini…

cs.LG2024

Spatial-temporal Forecasting for Regions without Observations

Xinyu Su, Jianzhong Qi, Egemen Tanin +2

Spatial-temporal forecasting plays an important role in many real-world applications, such as traffic forecasting, air pollutant forecasting, crowd-flow forecasting, and so on. Sta…

cs.LG2023

Urban Region Representation Learning with Attentive Fusion

Fengze Sun, Jianzhong Qi, Yanchuan Chang +3

An increasing number of related urban data sources have brought forth novel opportunities for learning urban region representations, i.e., embeddings. The embeddings describe laten…

cs.DB2023

Trajectory Similarity Measurement: An Efficiency Perspective

Yanchuan Chang, Egemen Tanin, Gao Cong +2

Trajectories that capture object movement have numerous applications, in which similarity computation between trajectories often plays a key role. Traditionally, the similarity bet…

cs.DB2023★ 10 cited

MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)

Jianzhong Qi, Zuqing Li, Egemen Tanin

Large language models (LLMs) are advancing rapidly. Such models have demonstrated strong capabilities in learning from large-scale (unstructured) text data and answering user queri…